There exist some methods for dealing with a related topic detecting and tracing technology.
For example, Korean Unexamined Patent Publication No. 2010-0105226 discloses a system and method for issue tracking and management, which effectively and efficiently processes business issues or problems that are generated in an organization such as an enterprise, a research institute, or the like.
As problems generated in a workplace become gradually complicated and diverse, it requires considerable early corresponding time to recognize the causes of the problems and to establish a solution thereto and it is difficult to determine an optimal solution for the problems without real time inter-department cooperation and cross-checking. In order to overcome the above disadvantages, the system and method for issue tracking and management has been proposed in the related art. The method includes proposing and disseminating an issue, searching similar issues, establishing alternatives and exchanging opinions in order to resolve the issue, and registering the processing result of the resolved issue.
This Korean patent publication provides to create tools that help a user propose an issue and directly search related examples or the like so that the user may easily resolve the issue.
Korean Unexamined Patent Publication No. 2009-0021350 presents an issue analyzing system and an issue data generation method, and more particularly, presents an issue analyzing system for extracting information included in input data to analyze an issue set by a user and to manage the analyzed issue and a method of generating issue analyzing data for issue analysis from various input data using the same.
The object of this Publication is to help automatically extracting a core word, a core image, metadata, and the like from various documents to search the issue input by the user.
U.S. Pat. No. 6,529,902, issued on Mar. 4, 2003 and entitled “method and system for off-line detection of textual topical changes and topic identification via likelihood based methods for improved language modeling”, provides an off-line segmentation of textual data that uses change-point methods, 2) to perform off-line topic identification of textual data, and 3) to provide an improved language modeling for off-line automatic speech decoding and machine translation. Specifically, a document is divided into segments of a specific size, a likelihood score is calculated for each segment to calculate a likelihood ratio, and a corresponding topic is assigned while dividing the document into segments when the likelihood ratio is greater than a threshold, indicating that a topic is converted.
The object of this patent is to correctly convert and assign a topic in a document based on a likelihood method.
Heung-seon Oh, Yunjeong Choi, Wookhyun Shin, Yoonjae Jeong, and Seong-hyon Myaeng, discloses “Trend Properties and a Ranking Method for Automatic Trend Analysis”, Information Science Academy Thesis: Software and Application, 2009. This thesis provides a method of defining four properties (variability, continuity, stability, and accumulation amount) from a trend curve composed of appearance frequencies and of determining the order of the trends using the same in order to quantify various aspects of trends.
Most of past researches on automatically analyzing trends from documents having time information such as patents, news, and blogs are focused on measuring the importance of given concept using appearance frequency information on words related to trends and on showing a trend line of the concept with time.
In the above thesis, utility of the respective properties is verified and it is analyzed which influence the combination of the properties has on determining the order through a series of experiments. It can be seen from the experiment results that it is easier to detect a specific trend when all of the four properties are combined with each other.
The object of this thesis is to better detect the trend. In the thesis, a method of determining the order of trends based on four properties is presented.
Michael Mathioudakis and Nick Koudas discloses “Twitter Monitor: Trend Detection over the Twitter Stream”, SIGMOD 2010. This thesis presents a system for detecting trends for Twitter stream analyzes Twitter in real time, which detects emerging topics based on the number of frequencies of a keyword, integrates related information on the respective topics, and provides a meaningful analysis result. The object of this thesis is to present a method of detecting the emerging topics as recent trends simply based on the number of frequencies of the keyword on recently frequently used Twitter.
Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo, discloses “Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors”, WWW 2010. This thesis relates to a method of detecting an accident such as earthquake in real time for tweet created on Twitter to trace the accident. A classifier in which a keyword and context information on tweet are used as features is used in order to detect a target accident and a time-space probability model by which the center of the place and the movement route of the target accident may be found is developed. According to the above system, it was possible to determine whether 96% of earthquake with magnitude 3 or more was generated in Japan from Twitter, to estimate the movement route of earthquake or typhoon, and to inform a target area of the movement route of earthquake or typhoon faster than the weather center.
The object of this thesis is to monitor Twitter in real time and to rapidly detect and inform an accident such as natural disaster or the like.
However, the methods for dealing with a related topic detecting and tracing technology are limited to detect a specific topic or trend itself. It is, therefore, not possible to automatically find topics in competition with a specific topic and to automatically find contents that the specific topic is associated and complexly mixed with other topics to spread out. Thus, when a user wishes to find competition topics or related topics to the specific topic to see a complex relationship between the topics, the user needs to directly search these topics.